The design of phenylglycine containing benzamidine carboxamides as potent and selective inhibitors of factor Xa

The Benzoylpiperidine Fragment as a Privileged Structure in Medicinal Chemistry: A Comprehensive Review

The phenyl(piperidin-4-yl)methanone fragment (here referred to as the benzoylpiperidine fragment) is a privileged structure in the development of new drugs considering its presence in many bioactive small molecules with both therapeutic (such as anti-cancer, anti-psychotic, anti-thrombotic, anti-arrhythmic, anti-tubercular, anti-parasitic, anti-diabetic, and neuroprotective agents) and diagnostic properties. The benzoylpiperidine fragment is metabolically stable, and it is also considered a potential bioisostere of the piperazine ring, thus making it a feasible and reliable chemical frame to be exploited in drug design. Herein, we discuss the main therapeutic and diagnostic agents presenting the benzoylpiperidine motif in their structure, covering articles reported in the literature since 2000. A specific section is focused on the synthetic strategies adopted to obtain this versatile chemical portion.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Design, synthesis and biological evaluation of 1-phenanthryl-tetrahydroisoquinoline derivatives as novel p21-activated kinase 4 (PAK4) inhibitors

Functional versatility and elevated expression in cancers have promoted p21-activated kinase 4 (PAK4) as one of the first-in-class anti-cancer drug targets. In this study, a series of novel 1-phenanthryl-tetrahydroisoquinoline analogues have been designed and synthesized as a novel class of small-molecule PAK4 inhibitors to fit into the cavity of PAK4. All of the target compounds were evaluated for their in vitro PAK4 inhibitory activities and antiproliferative activities. Lead optimization identified all the derivatives with more potency than the lead compound, especially compound 21a. Moreover, compound 21a significantly induced the cell cycle in the G1/S phase, and inhibited migration and invasion of MCF-7 cells via the regulation of the PAK4-LIMK1-cofilin signaling pathway. A molecular modeling study showed possible novel binding modes between 21a and PAK4 and provided a structural basis for further structure-guided design of PAK4 inhibitors.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Prediction of factor Xa inhibitors by machine learning methods

Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors.

Structure-based combinatorial library design: methodologies and applications

Rational design of small focused libraries that are biased toward specific therapeutic targets is currently at the forefront of combinatorial library design. Various structure-based design strategies can be implemented in focused library design when the 3D structure of the target is available through X-ray or NMR determination. This review discusses the major methods and programs specifically developed for the purpose of designing combinatorial libraries under the constraint of the binding site of a biological target, with emphasis on their advantages and disadvantages. Examples of the successful application of these methodologies are highlighted, demonstrating their performances within the practical drug discovery process.

Design, synthesis, and SAR of amino acid derivatives as factor Xa inhibitors

A series of potent and selective factor Xa inhibitors was synthesized using various readily available amino acids as central templates. The most potent compound displays IC(50) of 3 nM.